Author:
Jiang Huiming,Luo Jinhai,Shao Yunfei,Ma Qianxi,Pan Honghai
Reference28 articles.
1. Lei, Y., Li, N., Guo, L., et al.: Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mech. Syst. Signal Process. 104, 799–834 (2018)
2. El-Thalji, I., Jantunen, E.: A summary of fault modelling and predictive health monitoring of rolling element bearings. Mech. Syst. Signal Process. 60–61, 252–272 (2015)
3. Kumar, A., Kumar, R.: Role of signal processing, modeling and decision making in the diagnosis of rolling element bearing defect: a review. J. Nondestruct. Eval. 38(1), 5 (2019)
4. Ali, J.B., Chebel-Morello, B., Saidi, L., et al.: Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mech. Syst. Signal Process. 56–57, 150–172 (2015)
5. Rai, A., Kim, J.M.: A novel health indicator based on the Lyapunov exponent, a probabilistic self-organizing map, and the Gini-Simpson index for calculating the RUL of bearings. Measurement 164, 108002 (2020)
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献